test_fifosizing.py

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import pkg_resources as pk

import pytest

import json
import shutil
from brevitas.export.onnx.generic.manager import BrevitasONNXManager
from qonnx.transformation.general import GiveUniqueNodeNames

import finn.builder.build_dataflow as build
import finn.builder.build_dataflow_config as build_cfg
from finn.analysis.fpgadataflow.dataflow_performance import dataflow_performance
from finn.transformation.fpgadataflow.derive_characteristic import (
    DeriveCharacteristic,
    DeriveFIFOSizes,
)
from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
from finn.transformation.fpgadataflow.insert_dwc import InsertDWC
from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
from finn.util.basic import make_build_dir
from finn.util.test import get_trained_network_and_ishape


def custom_step_fifosize(model, cfg):
    model = model.transform(InsertDWC())
    model = model.transform(GiveUniqueNodeNames())
    model = model.transform(
        PrepareIP(cfg._resolve_fpga_part(), cfg._resolve_hls_clk_period())
    )
    model = model.transform(HLSSynthIP())
    model = model.transform(PrepareRTLSim())
    period = model.analysis(dataflow_performance)["max_cycles"] + 10
    model = model.transform(DeriveCharacteristic(period))
    model = model.transform(DeriveFIFOSizes())
    return model


def fetch_test_model(topology, wbits=2, abits=2):
    tmp_output_dir = make_build_dir("build_fifosizing_%s_" % topology)
    (model, ishape) = get_trained_network_and_ishape(topology, wbits, abits)
    chkpt_name = tmp_output_dir + "/model.onnx"
    BrevitasONNXManager.export(model, ishape, chkpt_name)
    return tmp_output_dir


@pytest.mark.slow
@pytest.mark.vivado
def test_fifosizing_linear():
    tmp_output_dir = fetch_test_model("tfc")
    steps = build_cfg.default_build_dataflow_steps
    steps.insert(10, custom_step_fifosize)
    cfg = build_cfg.DataflowBuildConfig(
        output_dir=tmp_output_dir,
        auto_fifo_depths=False,
        target_fps=10000,
        synth_clk_period_ns=10.0,
        board="Pynq-Z1",
        rtlsim_batch_size=100,
        shell_flow_type=build_cfg.ShellFlowType.VIVADO_ZYNQ,
        generate_outputs=[
            build_cfg.DataflowOutputType.ESTIMATE_REPORTS,
            build_cfg.DataflowOutputType.STITCHED_IP,
            build_cfg.DataflowOutputType.RTLSIM_PERFORMANCE,
        ],
        steps=steps,
        default_mem_mode=build_cfg.ComputeEngineMemMode.DECOUPLED,
    )
    build.build_dataflow_cfg(tmp_output_dir + "/model.onnx", cfg)
    with open(tmp_output_dir + "/report/estimate_network_performance.json") as f:
        est_data = json.load(f)
    with open(tmp_output_dir + "/report/rtlsim_performance.json") as f:
        sim_data = json.load(f)
    assert (
        float(sim_data["throughput[images/s]"])
        / float(est_data["estimated_throughput_fps"])
        > 0.9
    )
    shutil.rmtree(tmp_output_dir)


def test_fifosizing_residual():
    model_fname = pk.resource_filename(
        "finn.qnn-data", "testcase/residual_testcase.onnx"
    )
    steps = build_cfg.default_build_dataflow_steps[8:]
    tmp_output_dir = make_build_dir("build_fifosizing_residual")
    cfg = build_cfg.DataflowBuildConfig(
        output_dir=tmp_output_dir,
        auto_fifo_depths=True,
        auto_fifo_strategy="largefifo_rtlsim",
        synth_clk_period_ns=10.0,
        board="Pynq-Z1",
        verbose=True,
        rtlsim_batch_size=1,
        verify_save_rtlsim_waveforms=True,
        shell_flow_type=build_cfg.ShellFlowType.VIVADO_ZYNQ,
        generate_outputs=[
            build_cfg.DataflowOutputType.STITCHED_IP,
            build_cfg.DataflowOutputType.RTLSIM_PERFORMANCE,
        ],
        steps=steps,
        default_mem_mode=build_cfg.ComputeEngineMemMode.DECOUPLED,
        # start_step="step_set_fifo_depths",
        # stop_step="step_set_fifo_depths"
    )
    build.build_dataflow_cfg(model_fname, cfg)